Synthetic mosquito oviposition attractants are sorely needed for surveillance and control programs for Culex species, which are major vectors of pathogens causing various human diseases, including filariasis, encephalitis, and West Nile encephalomyelitis. We employed novel and conventional chemical ecology approaches to identify potential attractants, which were demonstrated in field tests to be effective for monitoring populations of Cx. p. quinquefasciatus in human dwellings. Immunohistochemistry studies showed that an odorant-binding protein from this species, CquiOBP1, is expressed in trichoid sensilla on the antennae, including short, sharp-tipped trichoid sensilla type, which house an olfactory receptor neuron sensitive to a previously identified mosquito oviposition pheromone (MOP), 6-acetoxy-5-hexadecanolide. CquiOBP1 exists in monomeric and dimeric forms. Monomeric CquiOBP1 bound MOP in a pH-dependent manner, with a change in secondary structure apparently related to the loss of binding at low pH. The pheromone antipode showed higher affinity than the natural stereoisomer. By using both CquiOBP1 as a molecular target in binding assays and gas chromatography-electroantennographic detection (GC-EAD), we identified nonanal, trimethylamine (TMA), and skatole as test compounds. Extensive field evaluations in Recife, Brazil, a region with high populations of Cx. p. quinquefasciatus, showed that a combination of TMA (0.9 µg/l) and nonanal (0.15 ng/µl) is equivalent in attraction to the currently used infusion-based lure, and superior in that the offensive smell of infusions was eliminated in the newly developed synthetic mixture.

Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of–the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.